Joint acoustic-video fingerprinting of vehicles, part II

V. Cevher; F. Guo; A. C. Sankaranarayanan; R. Chellappa

doi:10.1109/ICASSP.2007.366344

Joint acoustic-video fingerprinting of vehicles, part II

V. Cevher, F. Guo, A. C. Sankaranarayanan, R. Chellappa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data.

Original language	English (US)
Title of host publication	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Pages	II749-II752
DOIs	https://doi.org/10.1109/ICASSP.2007.366344
State	Published - 2007
Externally published	Yes
Event	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States Duration: Apr 15 2007 → Apr 20 2007

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2
ISSN (Print)	1520-6149

Other

Other	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/Territory	United States
City	Honolulu, HI
Period	4/15/07 → 4/20/07

Keywords

Acoustic applications
Acoustic signal processing
Intelligent sensors
Object recognition
Pattern recognition

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2007.366344

Cite this

Cevher, V., Guo, F., Sankaranarayanan, A. C., & Chellappa, R. (2007). Joint acoustic-video fingerprinting of vehicles, part II. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 (pp. II749-II752). Article 4217517 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2). https://doi.org/10.1109/ICASSP.2007.366344

Joint acoustic-video fingerprinting of vehicles, part II. / Cevher, V.; Guo, F.; Sankaranarayanan, A. C. et al.
2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07. 2007. p. II749-II752 4217517 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cevher, V, Guo, F, Sankaranarayanan, AC & Chellappa, R 2007, Joint acoustic-video fingerprinting of vehicles, part II. in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07., 4217517, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2, pp. II749-II752, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, Honolulu, HI, United States, 4/15/07. https://doi.org/10.1109/ICASSP.2007.366344

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abstract = "In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data.",

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year = "2007",

doi = "10.1109/ICASSP.2007.366344",

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N2 - In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data.

AB - In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data.

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ER -

Joint acoustic-video fingerprinting of vehicles, part II

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Keywords

ASJC Scopus subject areas

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